{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.utils import np_utils\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Conv2D,MaxPooling2D,Flatten\n",
    "\n",
    "width=28\n",
    "height=28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
    "x_train=x_train.reshape(60000,width,height,1).astype('float32')/255.0\n",
    "x_test=x_test.reshape(10000,width,height,1).astype('float32')/255.0\n",
    "\n",
    "x_val=x_train[50000:]\n",
    "y_val=y_train[50000:]\n",
    "x_train=x_train[:50000]\n",
    "y_train=y_train[:50000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_val=np_utils.to_categorical(y_val)\n",
    "y_test=np_utils.to_categorical(y_test)\n",
    "\n",
    "model=Sequential()\n",
    "model.add(Conv2D(32,(3,3),activation='relu',input_shape=(width,height,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(32,(3,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/30\n",
      "50000/50000 [==============================] - 13s 263us/step - loss: 0.5765 - accuracy: 0.8279 - val_loss: 0.1845 - val_accuracy: 0.9473\n",
      "Epoch 2/30\n",
      "50000/50000 [==============================] - 10s 205us/step - loss: 0.1654 - accuracy: 0.9508 - val_loss: 0.1238 - val_accuracy: 0.9636\n",
      "Epoch 3/30\n",
      "50000/50000 [==============================] - 10s 207us/step - loss: 0.1125 - accuracy: 0.9661 - val_loss: 0.0990 - val_accuracy: 0.9709\n",
      "Epoch 4/30\n",
      "50000/50000 [==============================] - 11s 213us/step - loss: 0.0884 - accuracy: 0.9735 - val_loss: 0.0771 - val_accuracy: 0.9781\n",
      "Epoch 5/30\n",
      "50000/50000 [==============================] - 11s 221us/step - loss: 0.0745 - accuracy: 0.9771 - val_loss: 0.0683 - val_accuracy: 0.9807\n",
      "Epoch 6/30\n",
      "50000/50000 [==============================] - 11s 210us/step - loss: 0.0649 - accuracy: 0.9797 - val_loss: 0.0644 - val_accuracy: 0.9809\n",
      "Epoch 7/30\n",
      "50000/50000 [==============================] - 11s 212us/step - loss: 0.0574 - accuracy: 0.9819 - val_loss: 0.0713 - val_accuracy: 0.9780\n",
      "Epoch 8/30\n",
      "50000/50000 [==============================] - 12s 232us/step - loss: 0.0516 - accuracy: 0.9839 - val_loss: 0.0638 - val_accuracy: 0.9826\n",
      "Epoch 9/30\n",
      "50000/50000 [==============================] - 12s 248us/step - loss: 0.0470 - accuracy: 0.9850 - val_loss: 0.0532 - val_accuracy: 0.9850\n",
      "Epoch 10/30\n",
      "50000/50000 [==============================] - 13s 257us/step - loss: 0.0415 - accuracy: 0.9872 - val_loss: 0.0542 - val_accuracy: 0.9847\n",
      "Epoch 11/30\n",
      "50000/50000 [==============================] - 12s 240us/step - loss: 0.0391 - accuracy: 0.9877 - val_loss: 0.0566 - val_accuracy: 0.9837\n",
      "Epoch 12/30\n",
      "50000/50000 [==============================] - 13s 259us/step - loss: 0.0354 - accuracy: 0.9889 - val_loss: 0.0583 - val_accuracy: 0.9833\n",
      "Epoch 13/30\n",
      "50000/50000 [==============================] - 13s 252us/step - loss: 0.0327 - accuracy: 0.9898 - val_loss: 0.0673 - val_accuracy: 0.9809\n",
      "Epoch 14/30\n",
      "50000/50000 [==============================] - 12s 239us/step - loss: 0.0304 - accuracy: 0.9902 - val_loss: 0.0501 - val_accuracy: 0.9859\n",
      "Epoch 15/30\n",
      "50000/50000 [==============================] - 13s 252us/step - loss: 0.0276 - accuracy: 0.9916 - val_loss: 0.0491 - val_accuracy: 0.9866\n",
      "Epoch 16/30\n",
      "50000/50000 [==============================] - 12s 234us/step - loss: 0.0261 - accuracy: 0.9919 - val_loss: 0.0476 - val_accuracy: 0.9874\n",
      "Epoch 17/30\n",
      "50000/50000 [==============================] - 12s 235us/step - loss: 0.0235 - accuracy: 0.9927 - val_loss: 0.0491 - val_accuracy: 0.9862\n",
      "Epoch 18/30\n",
      "50000/50000 [==============================] - 12s 244us/step - loss: 0.0220 - accuracy: 0.9934 - val_loss: 0.0509 - val_accuracy: 0.9866\n",
      "Epoch 19/30\n",
      "50000/50000 [==============================] - 13s 255us/step - loss: 0.0202 - accuracy: 0.9936 - val_loss: 0.0474 - val_accuracy: 0.9870\n",
      "Epoch 20/30\n",
      "50000/50000 [==============================] - 13s 259us/step - loss: 0.0197 - accuracy: 0.9939 - val_loss: 0.0507 - val_accuracy: 0.9864\n",
      "Epoch 21/30\n",
      "50000/50000 [==============================] - 15s 293us/step - loss: 0.0176 - accuracy: 0.9949 - val_loss: 0.0481 - val_accuracy: 0.9884\n",
      "Epoch 22/30\n",
      "50000/50000 [==============================] - 15s 309us/step - loss: 0.0164 - accuracy: 0.9950 - val_loss: 0.0486 - val_accuracy: 0.9872\n",
      "Epoch 23/30\n",
      "50000/50000 [==============================] - 15s 298us/step - loss: 0.0147 - accuracy: 0.9956 - val_loss: 0.0494 - val_accuracy: 0.9868\n",
      "Epoch 24/30\n",
      "50000/50000 [==============================] - 16s 311us/step - loss: 0.0146 - accuracy: 0.9956 - val_loss: 0.0473 - val_accuracy: 0.9870\n",
      "Epoch 25/30\n",
      "50000/50000 [==============================] - 15s 303us/step - loss: 0.0127 - accuracy: 0.9968 - val_loss: 0.0492 - val_accuracy: 0.9883\n",
      "Epoch 26/30\n",
      "50000/50000 [==============================] - 16s 319us/step - loss: 0.0124 - accuracy: 0.9965 - val_loss: 0.0450 - val_accuracy: 0.9882\n",
      "Epoch 27/30\n",
      "50000/50000 [==============================] - 17s 344us/step - loss: 0.0114 - accuracy: 0.9966 - val_loss: 0.0501 - val_accuracy: 0.9871\n",
      "Epoch 28/30\n",
      "50000/50000 [==============================] - 17s 341us/step - loss: 0.0109 - accuracy: 0.9970 - val_loss: 0.0466 - val_accuracy: 0.9883\n",
      "Epoch 29/30\n",
      "50000/50000 [==============================] - 19s 374us/step - loss: 0.0096 - accuracy: 0.9974 - val_loss: 0.0465 - val_accuracy: 0.9884\n",
      "Epoch 30/30\n",
      "50000/50000 [==============================] - 18s 352us/step - loss: 0.0089 - accuracy: 0.9976 - val_loss: 0.0564 - val_accuracy: 0.9870\n"
     ]
    }
   ],
   "source": [
    "hist=model.fit(x_train,y_train,epochs=30,batch_size=32,validation_data=(x_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAAEKCAYAAACYKLs6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABKSklEQVR4nO3deXicVdn48e+dmcmeJk3SfacthbK0QIsIClVkKYiURdn3VUXBhRdE31dw+VkV3LGVfVFEBKooCBa0VFlbSgqldKdt0jVJ26TZZ7l/f5xnkkkySaZpJmky9+e6zvXsT86TaefOWZ5zRFUxxhhj+rO0vs6AMcYYs78smBljjOn3LJgZY4zp9yyYGWOM6fcsmBljjOn3LJgZY4zp95IazETkdBFZLSLrROT2OMdniUiViJR46f+SmR9jjDHxichDIrJTRFZ0cFxE5Ffe9/l7InJ0zLG43/UiUigiC0VkrbccnKz8Jy2YiYgPuBeYDUwFLhKRqXFO/Y+qTvfS95KVH2OMMZ16BDi9k+Ozgcleuh6YB11+198OvKKqk4FXvO2kSGbJ7FhgnapuUNUm4Eng7CT+PGOMMd2kqouBXZ2ccjbwmDpvAgUiMoLOv+vPBh711h8F5iQl84A/WTcGRgGlMdtlwMfinPdxEVkObAW+qaoftD1BRK7H/SUAcEx2dna3M6UaIhJpIi0tExFrMjTGpIa6ujoFlsXsuk9V79uHW8T7Th/Vwf7od/0wVd0GoKrbRGToPmc8QckMZhJnX9uxs5YB41S1RkTOAP6CK8K2vsj9wu8DyMnJ0dra2m5nqrx8AR98cC7HHPM6eXlHdfs+xhjTn4hIvarO2J9bxNmnnezvVckMZmXAmJjt0bjSVzNVrY5Zf0FEfisixapakaxM+f35AIRCVcn6EcaYFKMKwSDU1bWk2tqWZWfr9fUQCrVPwWD7fRdfDDfc0GeP2dF3enoH+wF2iMgIr1Q2AtiZrMwlM5gtASaLyARgC3AhcHHsCSIyHNihqioix+La8CqTmKfmYBYOWzAzpr+rrYVNm2DjRvjoI7eMru/cCYEAZGR0nQDCYRcwwuGW1Ha7bcCKTeHwvuU9LQ1yciAz0+UzEAC/36XY9djttL5tGXkOuElEnsRVI1Z5Qaqcjr/rnwOuAOZ6y78mK3NJC2aqGhKRm4CXAB/wkKp+ICI3esfnA+cDXxSREFAPXKhJHsbf57OSmTG9qbERdu/uPNXUuHNFWqe0tNbb4IJUNHDtbPN3fkYGjB/v0mGHuWDU2NiSmppg716oqGi9H1yw8PlaUtttn88FlaIiGDPGBaLs7PgpK8sdj6bs7NbLnBxIT295pgOBiPwRmAUUi0gZ8F0gAM3f1y8AZwDrgDrgKu9Y3O9677ZzgadE5BpgM/D5pOW/v00Bs79tZk1N5bz++lAmTfoVo0d/pQdzZszA0NAAW7fCli3xU0WFK4WoQiTSsmy7Hom4IFVf3/nPy82FvDy3rho/Re+tCkOHtgSsCRNa1sePh2HD+rz0csASkTpVzenrfCRLMqsZD0jWZmb6q4YG2LXLpbq6lpJGU1P89dhlvNT22M6dLlhVxqnoz86GUaNcOvLIliqvaOkp3rqIC1SDB3ecCgpcaceY/ZVywSwtLZ20tExrMzN9JhKBPXugvNyVcsrLXaqsbAlW8VJXJZzOpKV13F6Unu6W48bB8ce3BK3YlJ9/YFWJGdNWygUzcO1mVjIz3VFTA+vWQWmpCy4NDa5U09kyGriiwStaTRdPRoZrkyksdGnSpJb1aBo82LW5RANRNMVut133p+T/dJNKUvKfuN9vwcx0rL7eBay1a9unbdu6vl7EBZDMTLcsKIAhQ2DyZFfyGTLEpeLi1uvFxa46zxiz7yyYmQFH1fVY27YNtm93JaKaGteNO7psm6L7S0tdijV0qAtEp53mlpMnu84G2dktASt26fdblZwxvS1lg5m1mfVPNTWwebN7t2jr1paAtX17y/q2bV23L8V2m87JcR0VcnLgpJNaAlY05ef3zrMZY7ovJYOZz5dPY+OWvs6GaSMYdB0dNm9uCVibNrVe3xVnGNTCQhg+3KWPf7xlfcQItxwyxHX9jgatrCwrOe2LhlADy7cvZ+nWpSzZuoSNezYyetBoxheMZ3zBeMblj2N8wXjG5o8lw5/RJ3msD9azYfcGNuzewPrd69mwewPba7YjIvjER5qk4Uvztay32VecXcyY/DGMGTSmeZkVyEroZzeGGtlctZmNezaycc9GNlVtYlPVJmqbaolohLCGCUfCHa6n+9KZOHgik4smM7lwMpOLJjOpcBLZAatz3hcpGcysmrF3BINQUgJvvgllZVBd7ar/qqtbUux2Q0P7e+Tmul5248bBcce1rI8dC6NHu/eKMvrm+xOAUCREZV0l5XXllNeWNy931u6kvK6ciroKctNzm7/4o2lk3kj8aT333y8UCVHbVEttsJa6YB21TW4ZjAQZlDGIgswCCjILGJQxiLROBtgORUKsLF/Jki1LWLLVpfd3vE8wEgRgWM4wJhZO5LXS13hyxZOEtXVPlpF5I1sFuKKsIrID2eSk55ATyIm7nh3IJtOfSTAcJBgJ0hRual4Phr3tmPUte7e0Clrrd61nW03rxszc9FxG5Y0CiBtEIhohHAkT1jChSIjqxmraKs4ubhXcxgwaw6CMQZRWl7YKXFv3thqlD5/4GJM/hrz0PHxp8YNnmqSR7ksnzZ9GfbCe59c+z46SHa3uM3rQaBfcvAA3uXAyR404irH5YxP/h5FCUu6laYB1677O1q33ceKJNT2UKwOubeqNN+D1191yyZKW6r6MDFc6GjSoJcXbLihwgSoatAoKeq8UFQwH2V6znV31u6isr3TLusqW9Tb7ymvL2VW/C40zpqogFGYVUpxdzN6mvR1+4TUHuPzxDMsdRkOooTkYNQemYG1zcGq7Hj03Gmy6IkhzcMvPzG8V5D7a/RHvbn+XumAdAPkZ+cwYOYOZI2cyc9RMZo6cyehBoxHvAwlFQmyp3sKmqk3NX+6xqbS6lFAktJ+fSsdGDxrNQYMPYuLgia2XhRMpyipqzmciGkONlFWXUVpdSmlVKaXVpWyu2txqe0/DHgD8aX7G5o9tFbR74g+V6sZq1u1ax9rKtazdtZY1lWtYu2stayvXUlnvXv679fhb+ckpP9nne8PAf2k6JYPZxo13sXHjnZx4YpC0HvzrOJXU1SnLVu7hnSV+3nkrkzde97Nurfvy8Pvh6KNdz73jj3dVf6NH93GGO1EXrOPet+/lx6/9uPlLo63sQDaFWYUUZRW5ZXYRQ7KHMCR7CENzhjIkx61Hl0XZRa2+0GKrouJ9+W/du7VVUPSJr7nUkhPIabXeXKLxd13iCaQFqG6sZk/DntapcQ9VDVWt9o0aNMoFLi94TSqc1GkprivhSLhVwG1baowNzI3hRgJpAQK+QPMy3Zced3147nDGF4wn05/Z7bx1R01TDVUNVQzPHY4vzderP3t3/W7W7lpLYVYhkwondeseFswOMD0RzEpLf8H69V/jhBMqCQQKeyhnA8vevS3tVJs2wUcblQ+2rmdVwyK2ZSyiYdirkF/WcoGmEZBMsvyZ5GRmkhXIJNPfkgqzChmVN4qReSNbloPccl//iu4pjaFG7l92Pz/8zw/ZXrOd0yaexnmHnkdRdlFz4IquJ/uLszHUSGV9JVn+LHLScwikBfrkd2IGroEezFKyWBI7pFWqB7NIBFavdlWCS5bA0qWwZg3s2qVQtBbGvQrjF8GERTDZVZVlhYdxuP8kjiya6QZczW+gMdxAQ6iBxlAjDaEGGrzthlAD9cF6SqtKeavsLcrrytvlId2X3hzkhucOJzc9t10ppG2JIyeQw5TiKYzMG7nPzxyKhHhs+WPc9epdbK7azInjTuSp85/ik+M+uZ+/ze7L8Gd061mMMU7KB7NUoupKWdHAtWQJvPOOK4UBZOcFGXP6UxSe9DzBrEXsxTWqD80azqcOmsWs8Scxa/wsphRN6XapoTHUyPaa7WzZu4Wte7eypdpbetsfVnzYXCVV21RLfajzPvaTCydz0jiXr5PGn8ToQR3XZ0Y0wp9W/InvLvoua3etZebImdx/1v2cctApVgoypp9L6WA20N81q6qCt95yvQnffNMFrwpv2tP0dJg2DS67DKYdU8/mood4fMNPWF29mRG5I/js+FkuQIw7iYOLDu6xL/sMfwbjCsYxrmBcQudHNEJ9sL5de8vepr2UbC9h0cZFPP3h0zzw7gMATBw8sTnfs8bPYkz+GFSVv67+K//77/9lxc4VHDH0CP5ywV/43JTPWRAzZoBIyTaz6uqlLFs2k8MP/yvFxZ/roZztv4hGKNlewpItS2gINXTYNTm6HowEmVQ4ic8e/FkOKz6C1auFN95wgeuNN2DlSlcaE4GpU+HYY2HmTJeOOAIatIp5S+fx8zd/zs7anRw/5ni+/clvM3vS7H71JR+OhHlvx3ss2riIVze9yuJNi9ndsBuACQUTyE3P5f2d73Nw0cHcNesuvnDYF/arY4Mx/dFAbzNLyWBWV7eWt98+mEMOeYzhwy/roZx1z6Y9m1i4YSELNyzklQ2vdNibLvpeSrRHlz8tQDjoo7LJtWNJ9Th01Wdh9VkUVM3i4zMzOO4415Pw2GNbj2JRXlvOL9/6Jb95+zdUNVZx2sTTuOOTd/DJsZ/sV0GsIxGN8P6O91m0cRGLNi2irLqML874IpdPu7xH3+0ypj+xYHaA6Ylg1tS0k9dfH8akSb9m9OibeihnialqqOLfG//NwvUugK3dtRaAEbkjOGXiKZxy0Cl8cuwnycvIaxW80DRKSmDhQnj5Zfjvf72XjPO2MWrW8/in/o1t2Qtp0npy03M5deKpnHXwWZwx+QyG5gwFoKy6jLtfv5v73rmPhlAD5x56Lt/6xLc4ZuQxvfo7MMb0voEezFLyz9TebjOLaIR5S+bxxIoneKvsLcIaJieQw0njT+JLM7/EKQedwtQhU9uVij76yAWuhQvhX/9qmTTx8MPhi1+Ez3wGPvGJEQwadC1wLfXBev710b/425q/8fc1f+fZD59FEI4bfRzjC8bz9MqniWiES4+8lNtOuI1DhxzaK89vjDHJlpIlM4BXX81k9OivMnFi996mT1R5bTmX/+VyXlz3IkePOJrZk2ZzykGn8PExHyfdl97q3EgEXn0VnnrKBbD1693+kSPhlFNc8Dr5ZDfmYFdUlZLtJfxtzd/425q/sapiFVdMu4JvHv9NxheM7/kHNcYc0BIpmYnI6cAvAR/wgKrObXN8MPAQMBFoAK5W1RUiMgX4U8ypBwH/p6q/EJE7geuA6Hs5d6jqCz3xTK3ylqrB7LXXhlFcPIcpU37XA7mKb/GmxVz0zEVU1lXyi9N/wQ3H3BC3TWrdOnjsMZc2bXLjEX7qUy54nXIKHHKIDYxrjNk/XQUzEfEBa4BTgDJgCXCRqq6MOeenQI2q3iUihwD3qurJce6zBfiYqm7yglmNqt7d4w8VIyWrGSG5gw2HI2F+9N8f8d1F32Xi4Ik8f+3zTB8+vdU5VVXw5z/Do4+69i8RF7h+9COYM8eN7G6MMb3oWGCdqm4AEJEngbOBlTHnTAV+BKCqq0RkvIgMU9XYUZJPBtar6qZeyjeQ4sEsGW1mO2p2cOmCS3l5w8tcfMTFzD9zPnkZeQCEw67t65FHYMECNwjvIYfA3Llw6aUwalSPZ8cYYxI1CoidmrYM+Fibc5YD5wL/FZFjgXHAaCA2mF0I/LHNdTeJyOXAUuAbqrq7JzMOKRzMfL6eL5n966N/cfEzF1PVWMUDZz3A1UddjYgQDsPdd8Ovfw1btriR4K+80qWZM60K0RjTK/wisjRm+z5VvS9mO943Udt2qLnAL0WkBHgfeBdonhpBRNKBzwHfirlmHvB9717fB+4Bru7mM3QoZYOZ359PXd22rk9MQDgS5nuvfo/vL/4+U4qn8PLlL3P40MMBN+LGxRe7Dh2nnQY//zmcdRZk9u6A38YYE1LVGZ0cLwPGxGyPBlrNXaSq1cBVAOI6AHzkpajZwLLYasfYdRG5H/h7dx+gMykdzHqiZLZ171YuefYSFm1cxBXTruDeM+4lJ921sb71Fnz+87BzJzzwAFxzzX7/OGOMSZYlwGQRmYDrwHEhcHHsCSJSANSpahNwLbDYC3BRF9GmilFERqhqtORwDrAiGZlP6WC2v21mC9cv5JJnL6E2WMvDZz/MldOvBNwQUvPmwS23uHaw119383sZY8yBSlVDInIT8BKua/5DqvqBiNzoHZ8PHAo8JiJhXMeQ5j/RRSQb1xPyhja3/omITMdVM26Mc7xHpGww8/nyCYdrUA3jepImLrZa8dAhh7Lo84uYOmQqALW1cOON8PvfwxlnwOOPQ2FqzzJjjOknvPe/Xmizb37M+hvA5A6urQOK4uzvlTEDUzaY+f0FAIRC1QQCgxO+bkfNDi5+9mL+9dG/2lUrrlkD550HH3wA3/sefPvbkGbj2RpjTNKlcDCLnaAzsWC2aOMiLnrmIvY07OGhzz3EVUdd1XxswQLXOzEQgBdfhFNPTUaujTHGxJOy5YZ9GZ8xohH+33/+Hyc/djL5Gfm8fe3bzYEsFILbboNzz4UpU2DZMgtkxhjT26xk1kWPxoq6Ci5bcBkvrnuRiw6/iN999nfNL0Hv3g3nnOPGU/ziF123+4yMpGfdGGNMGykbzHy+roPZa5tf44KnL6CiroL5Z87n+mOubzW24s03w2uvuTEVL+vbadGMMSalpXw1Y7xgpqrc/frdnPTISWT6M3njmje4YUbrQYIXLnQ9FW+/3QKZMcb0tZQtmbUEsz3tjl3xlyt4/L3HOe/Q83jwcw+Sn5nf6nhdnet+P3my67FojDGmb6V8MGvbAeTva/7O4+89zh2fuIMffPoHcads+f73YcMGN2iwDUtljDF9L6nVjCJyuoisFpF1InJ7J+fNFJGwiJyfzPzESkvLQCSjVTVjQ6iBW168hUOLD+XOWXfGDWTvvecGDb7ySjfnmDHGmL6XtJKZN0HbvcRM9CYiz8VO9BZz3o9xQ6j0qrbjM97z+j2s372ehZctJOALtDs/HIbrr3ej3t+d1GnmjDHG7ItklsyaJ3rzBqWMTvTW1leAZ4CdScxLXLHBbHPVZn74nx9y3qHn8ZmDPhP3/Hnz3ODBP/85FLUbtMUYY0xfSWYwizfRW6vpJ0VkFG4U5fl0QkSuF5GlIrI0FAp1duo+iR1s+Jv//CYA95x6T9xzy8rgjjvcbNCXXNJjWTDGGNMDkhnMEpno7RfAbaoa7uxGqnqfqs5Q1Rl+f8/VjEYn6Hxlwyv8eeWf+dYnvsW4gnFxz/3KVyAYdKUzm0zTGGMOLMnszdjlRG/ADOBJr6NFMXCGiIRU9S9JzFczvz+f6pqtfPXFr3LQ4IO49YRb4563YAH85S8wdy5MnNgbOTPGGLMvkhnMupzoTVUnRNdF5BHg770VyMAFsyc/2srK8iqeu/A5Mv3t+9lXV7tS2ZFHwte/3ls5M8YYsy+SFswSnOitT+0O+nlwfRWzJ83mswd/Nu453/42bN0KzzzjRsQ3xhhz4BHVts1YB7acnBytra3tkXud//vpPLdhOSu+9CEHFx/S7vibb8Lxx8OXvwy//nWP/EhjjOkTIlKnqjl9nY9kSdmxGV8vfZ1n1i/n86NhQv6wdseDQfdO2ciR8MMf9kEGjTHGJCwlh7MKR8Lc9MJNjMgZzKXjdsedoPOee+D9913nj0GD+iijxhhjEpKSJbMHlj3Au9vf5a7jryTL1358xvXr4a673Fxlc+b0TR6NMaa3dTUEoYgMFpEFIvKeiLwtIofHHNsoIu+LSImILI3ZXygiC0Vkrbcc3Pa+PSHlglllXSV3/OsOZo2fxblTZgPtp4H5xjdcZw9rJzPGpIqYIQhnA1OBi0RkapvT7gBKVPVI4HLgl22Of0pVp6vqjJh9twOvqOpk4BVvu8elXDD733//L1UNVfzq9F8RCBQA7YPZ0qVw7rkwalScGxhjzMCUyBCEU3EBCVVdBYwXkfadDlo7G3jUW38UmNNjOY6RUsFs2bZlzF86n5uOvYkjhh0Rd4JOVaiogKFD+yqXxhiTFP7osIBeur7N8S6HIASWA+cCiMixwDjcgBjgRnj6p4i80+bew1R1G4C3TMq3a8p0AFFVvvKPr1CcXcyds+4E4s9pVlsLjY1QXNwXuTTGmKQJtan+ayuRIQjnAr8UkRLgfeBdIDpg7gmqulVEhgILRWSVqi7e30wnKmWC2RPvP8Hrpa/z0OceoiCzAHBjM0LrkllFhVtaMDPGpJguhyBU1WrgKgBx4xB+5CVUdau33CkiC3DVlouBHSIyQlW3icgIkjRDSspUM54x+Qx+/Jkfc8X0K5r3+XyZiKRbMDPGmJghCEUkHTcE4XOxJ4hIgXcM4FpgsapWi0iOiOR55+QApwIrvPOeA6JfvFcAf01G5lOmZDY4azD/c8L/tNvfdoJOC2bGmFSU4BCEhwKPiUgYWAlc410+DFjgDRrvB55Q1Re9Y3OBp0TkGmAz8Plk5D9lgllHYuc0Aygvd0sLZsaYVKOqLwAvtNk3P2b9DWBynOs2ANM6uGclcHLP5rS9lKlm7Eh0TrOoaMlsyJA+ypAxxph9lvLBLF41o88H+fl9mCljjDH7xIJZnGBWVARpKf+bMcaY/iPlv7LbtplVVFh7mTHG9DcpH8zitZlZMDPGmP4l5YOZ319AOLwX1TBgwcwYY/ojC2bN4zPuBSyYGWNMf2TBLGZ8xkgEKistmBljTH9jL03HjJxfXw/hsAUzY4zpb1K+ZBY72HB09A97YdoYY/qXlA9msSUzG5fRGGP6JwtmMW1mFsyMMaZ/smBmJTNjjOn3Uj6YxbaZWTAzxpj+yYJZzASdFRWQkQE5OX2dK2OMMfsi5YMZtIzPGH1h2s0vZ4wxpr+wYEZ05Pw9NvqHMcb0UxbMaBlsuKLC3jEzxpj+yIIZLXOaWcnMGJPKROR0EVktIutE5PY4xweLyAIReU9E3haRw739Y0Tk3yLyoYh8ICI3x1xzp4hsEZESL52RjLxbMKMlmJWXWzAzxqQmEfEB9wKzganARSIytc1pdwAlqnokcDnwS29/CPiGqh4KHAd8uc21P1fV6V56IRn5t2CGC2aNjbXs2WPBzBiTso4F1qnqBlVtAp4Ezm5zzlTgFQBVXQWMF5FhqrpNVZd5+/cCHwKjei/rFswA12a2a5f7VVgwM8YMUH4RWRqTrm9zfBRQGrNdRvuAtBw4F0BEjgXGAaNjTxCR8cBRwFsxu2/yqiYfEpHB+/8o7Vkww5XMdu3KBCyYGWMGrJCqzohJ97U5Hu+lJG2zPRcYLCIlwFeAd3FVjO4GIrnAM8Atqlrt7Z4HTASmA9uAe/b3QeJJajBLoDHxbC9al3h/KXwimfnpiN+fT1WVi2IWzIwxKaoMGBOzPRrYGnuCqlar6lWqOh3XZjYE+AhARAK4QPYHVX025podqhpW1QhwP646s8clLZgl2Jj4CjDN+8VcDTyQrPx0xoKZMcawBJgsIhNEJB24EHgu9gQRKfCOAVwLLFbVahER4EHgQ1X9WZtrRsRsngOsSEbmkzk5Z3NjIoCIRBsTV0ZPUNWamPNzaF+k7RU+nwUzY0xqU9WQiNwEvAT4gIdU9QMRudE7Ph84FHhMRMK47/JrvMtPAC4D3veqIAHu8Hou/kREpuO+3zcCNyQj/8kMZvEaEz/W9iQROQf4ETAUODPejbyGyusB0tPT452yX/z+fKqrLZgZY1KbF3xeaLNvfsz6G8DkONf9l/htbqjqZT2czbiS2WaWSGMiqrpAVQ8B5gDfj3cjVb0v2mjp9/d8/I1WM+bmBsnI6PHbG2OMSbJkBrMuGxNjqepiYKKI9HrZKBrMCgsbevtHG2OM8YjIMyJypojsc2xKZjBLpDFxktdwiIgcDaQDlUnMU1w+Xz579gxh8OC63v7RxhhjWswDLgbWishcETkk0QuT1maWYGPiecDlIhIE6oELVLXXO4FES2YjR9YAw3r7xxtjjAFU9WXgZRHJBy4CFopIKa5L/+9VNdjRtdIHsWO/5OTkaG1tbY/eU1UZMWITH//4LhYsOLpH722MMQcCEalT1QN+6mERKQIuxfWO3Ar8AfgEcISqzuroumT2Zuw3RITq6iEUFKzr66wYY0zKEpFngUOAx4GzVHWbd+hPIrK0s2stmAH19VBfn0N+fq831xljjGnxG1X9V7wDqjqjswttbEag0othgwbt7NuMGGNMajtURAqiG978aV9K5EILZkBFhVsOGrSt8xONMcYk03Wquie6oaq7gesSudCCGbHBbEvfZsQYY1JbWvR1LWge4zehYZ+szYyWYJaXV9r5icYYY5LpJeApEZmPGzHqRuDFRC60YAaUl7tlbu7GPs2HMcakuNtwAxF/ETck4j9JcDYVC2a4kplIhOzsUlQjdGMkFWOMMfvJm/Nsnpf2iQUzXDDLz2/E5wsRDu/F78/v6ywZY0zKEZHJuFlUpgKZ0f2qelBX11oRBBfMCgsbAQiFqvo4N8YYk7IexpXKQsCngMdwL1B3KaFgJiI3i8ggcR4UkWUicmq3s3uAqaiAoqIQYMHMGGP6UJaqvoIbanGTqt4JfDqRCxMtmV2tqtXAqcAQ4CpgbndyeiBywSwCWDAzxpg+1OBN/7JWRG7yJm8emsiFiQazaL//M4CHVXU5Hcwq2h9VVMCQIW49HLZgZowxfeQWIBv4KnAMbsDhKxK5MNFg9o6I/BMXzF4SkTwgsu/5PPCoRoOZD7CSmTEmdYnI6SKyWkTWicjtcY4PFpEFIvKeiLwtIod3da2IFIrIQhFZ6y0Hd/CzfcAXVLVGVctU9SpVPU9V30wk74kGs2uA24GZqloHBHBVjf1eTQ00NcGQIQHAgpkxJjV5weReYDauN+FFIjK1zWl3ACWqeiRwOfDLBK69HXhFVScDr3jb7ahqGDgmdgSQfZFoMPs4sFpV94jIpcB3gAHxrR8d/WPoUNcL1IKZMSZFHQusU9UNqtoEPAmc3eacqbiAhKquAsaLyLAurj0beNRbfxSY00ke3gX+KiKXici50ZRI5hMNZvOAOhGZBvwPsAnXZbLfi47+MWRIAJGAtZkZYwYqv4gsjUnXtzk+Cogd06/M2xdrOXAugIgcC4wDRndx7bDovGTesrMOHYVAJa4H41le+mxCD5fISUBIVVVEzgZ+qaoPikhCjXIHumjJbMgQIRzOt5KZMWagCnUxJ1i86j1tsz0X+KWIlADv40pSoQSv7ZKqdrv5KtFgtldEvoWbxvqTXv1ooLs/9EASDWbFxVBRYcHMGJOyyoAxMdujga2xJ3ivaF0F4LVtfeSl7E6u3SEiI1R1m4iMADqcOFJEHiZOEFTVq7vKfKLVjBcAjbj3zbbjio8/TfDaA1psMPP7LZgZY1LWEmCyiEwQkXTgQuC52BNEpMA7BnAtsNgLcJ1d+xwt3euvAP7aSR7+DjzvpVeAQUBNIplPqGSmqttF5A/ATBH5LPC2qg6INrOKCvD7IT/fBTNrMzPGpCJVDYnITbhpWHzAQ6r6gYjc6B2fDxwKPCYiYWAlrqd7h9d6t56Lm9blGmAz8PlO8vBM7LaI/BF4OZH8JxTMROQLuJLYIlzd6K9F5FZVfTqR6w9kFRWuVCbigll9/fq+zpIxxvQJVX0BeKHNvvkx628AkxO91ttfCZzczSxNBsYmcmKibWbfxr1jthNARIbgouWACWYAPl8+odCePs2PMcakKhHZS+s2s+24Oc66lGgwS4sGMk8lA2TE/dhgZm1mxhjTd1Q1r7vXJhqQXhSRl0TkShG5Etc416442R+1DWbh8F7c/HDGGGN6k4icIyL5MdsFIjInkWsTCmaqeitwH3AkMA24T1UTKvod6NoGM1DC4b19midjjElR31XV5uoxVd0DfDeRCxOeadrrZfJMlyf2I5EIVFa2bjMDN6SVzTZtjDG9Ll4BK6E41elJcRrjmg8BqqqDEvkhB6rdu11Aa10ys/EZjTGmjywVkZ/hBi1W4CvAO4lc2Gkw25/GuP4g9oVpsGBmjDF97CvA/wJ/8rb/iRvYvksJVzMORC3jMrplNJjZi9PGGNP7VLWWDqaI6cqA6F7fXW1LZrFtZsYYY3qXN3lnQcz2YBF5KZFrLZhh1YzGGHOAKPZ6MAKgqrvpfMqYZhbMsGBmjDEHiIiINA9fJSLjSXAqmZRvM8vKguxst52WloWI39rMjDGmb3wb+K+IvOptnwi0nUQ0rpQPZtFSGYCIeOMzWjAzxpjepqovisgMXAArwU0XU5/ItUmtZhSR00VktYisE5F2PVRE5BIRec9Lr4vItGTmp622wQxsfEZjjOkrInItbh6zb3jpceDORK5NWjDzZqO+F5gNTAUuEpGpbU77CDhJVY8Evo8bMqvXlJdbMDPGmAPIzcBMYJOqfgo4CihP5MJklsyOBdap6gZVbQKeBM6OPUFVX/d6qwC8iZtqu9d0VDKzNjNjjOkTDaraACAiGaq6CpiSyIXJbDMbBZTGbJcBH+vk/GuAf8Q7ICLX4zUCpqenxzulWyoqWl6YjvL58mlo2NBjP8MYY0zCyrz3zP4CLBSR3cDWRC5MZjCTOPvidrEUkU/hgtkn4h1X1fvwqiBzcnIS6qbZlWAQqqrilcwKrJrRGGP6gKqe463eKSL/BvKBFxO5NpnVjGXAmJjt0cSJsCJyJPAAcLY3vXavqPR+krWZGWOMk0CnvXwR+ZuILBeRD0TkKm//FBEpiUnVInKLd+xOEdkSc+yMRPKiqq+q6nNeM1WXklkyWwJMFpEJwBbgQuDi2BO8l+OeBS5T1TVJzEs7bV+YjnJtZtWoRhBJ6XfKjTEpJKbT3im4wsgSEXlOVVfGnPZlYKWqniUiQ4DVIvIHVV0NTI+5zxZgQcx1P1fVu5OZ/6QFM1UNichNwEuAD3hIVT8QkRu94/OB/wOKgN+KCEBIVWckK0+xOgtmboLOGvz+fj3DjTHG7IvmTnsAIhLttBcbzBTIE/eFnQvsAkJt7nMysF5VNyU/yy2S+tK0qr4AvNBm3/yY9WuBa5OZh2bhMCxeDLNmgUiHwaz1BJ0WzIwxA4ZfRJbGbN/n9UeISqTT3m+A53BNRnnABaoaaXPOhcAf2+y7SUQuB5YC34jpxd5jUqce7ZFH4NOfhnfcPG+dl8xsfEZjzIATUtUZMante72JdNo7DTcyx0hcteJvRKT5r34RSQc+B/w55pp5wETv/G3APfvxDB1KnWB2/vluEMb73OcXDWZFRa1PsznNjDEpKpFOe1cBz6qzDjfwxSExx2cDy1R1R3SHqu5Q1bBXgrsfV53Z41InmOXnwwUXwBNPwN69lJe7XW1fW7OSmTEmRTV32vNKWBfiqhRjbca1iSEiw3AvNMe+mHsRbaoYRWREzOY5wIoezjeQSsEM4PrrobYWnnwy7ugfYBN0GmNSk6qGgGinvQ+Bp6Kd9qId93DDDh4vIu/jxlC8TVUrAEQkG9cT8tk2t/6JiLwvIu8BnwK+loz8i2qPvIPca3JycrS2trZ7F6vCtGmQkcFphUuoqoI332x9SmPjNt54YySTJ89j1Kgb49/HGGP6GRGpU9Wcvs5HsqRWyUwErrsOli6lYnNd3JKZtZkZY0z/k1rBDODSSyEzk4qy+rjBLDpBp1UzGmNM/5F6wWzwYPjCF6ioyaQ4P9jusE3QaYwx/U/qBTOg7rIbqCOH4i3L4x73+/MJBhOaQscYY8wBICWDWeXBHwegeNk/4x4vKDiRysrnCQZ7bdxjY4wx+yElg1lFpXvRvfijt+G999odHz36G0QidWzZMq+3s2aMMaYbUjKYlXs1iEP8e+D++9sdz809nMLC2WzZ8mvC4YbezZwxxph9lpLBrHlcxtNnwOOPQ11du3PGjPkmweBOdux4vJdzZ4wxZl+ldjC77hw33fTTT7c7p6DgU+TmHk1p6T20HxTaGGPMgSRlg1laGhSccTwcfHDz4MOxRIQxY26lvn41lZV/64NcGmOMSVTKBrPCQvD5vRFBXnsNPvig3XlDhpxPZuZ4Nm/+aR/k0hhjTKJSNpg1j/5xxRUQCMTtCJKW5mf06K9RXf0aVVVv9G4mjTHGJMyC2ZAhcM458Nhj0NC+5+Lw4Vfj9w+mtPTu3s2kMcaYhFkwAzc1zO7d8Mwz7c71+3MZOfKLVFQsoK5ube9l0hhjTMIsmAF86lMwcWLcjiAAo0Z9BZEAZWU/650MGmOM2ScpF8xUXTAbMiRmZ1oaXHstLF4Mq1e3uyYjYzjDh1/O9u2P0NS0s/cya4wxJiEpF8yqqyEYjDPL9JVXgt8ftyMIRIe4amDLlnuTnkdjjDH7JuWCWfML022D2fDhcPbZ8Mgj0NjY7rqcnEMoKjqLLVvuJRxuP2KIMcaYvmPBLNZ110FlJfzlL3GvHTPmVkKhSrZvfyRZ2TPGmD4jIqeLyGoRWScit8c5ni8ifxOR5SLygYhcFXNso4i8LyIlIrI0Zn+hiCwUkbXecnAy8m7BLNYpp8C4cR12BMnP/wR5eR+jtPRnqIaTl0ljjOllIuID7gVmA1OBi0RkapvTvgysVNVpwCzgHhFJjzn+KVWdrqozYvbdDryiqpOBV7ztHmfBLFZamiud/etfsG5du8Miwtixt9LQsJ7y8gXJzagxxvSuY4F1qrpBVZuAJ4Gz25yjQJ6ICJAL7AJCXdz3bOBRb/1RYE6P5TiGBbO2rroKfD64O/5L0sXFc8jMnEhp6U9R1eRk0hhjep5fRJbGpOvbHB8FlMZsl3n7Yv0GOBTYCrwP3KwtI7Er8E8ReafNvYep6jYAbzm0h56nlZQMZoEA5OV1cMLIkfDFL8Lvfgc//GG7wyI+xoz5Onv3vk1V1X+Tm1ljjOk5IVWdEZPatqdInGva/sV+GlACjASmA78RkUHesRNU9WhcNeWXReTEnst611IymBUXg8T72KJ+8Qu49FL4znfiltCGD7+SQKCY0lIbgNgYM2CUAWNitkfjSmCxrgKeVWcd8BFwCICqbvWWO4EFuGpLgB0iMgLAWyblZd2UDGatXpiOx+eDhx+GL3wBbr0VfvWrNoezGTnyy1RW/o3a2g+Tl1ljjOk9S4DJIjLB69RxIfBcm3M2AycDiMgwYAqwQURyRCTP258DnAqs8K55DrjCW78C+GsyMp+SwazD9rJYfj/8/vduEOKbb4b581sdHjXqy6SlZVJaek9yMmqMMb1IVUPATcBLwIfAU6r6gYjcKCI3eqd9HzheRN7H9Uy8TVUrgGHAf0VkOfA28LyqvuhdMxc4RUTWAqd42z1O+lsnhpycHK2tre329YccAtOmwZ/+lOAFTU1w7rnw/PPw4INw9dXNh9as+RLbtj3IMce8TW7utG7nyRhjkk1E6lQ1p6/zkSxWMutKejo8/TSceqobv/H3v28+NHbsHaSnD6Wk5GRqapb3fGaNMcYkJKWCWTgMu3btYzADyMx0o4LMmuUm8/SKdZmZo5k+fRE+X5YFNGOM6UNJDWYJDI1yiIi8ISKNIvLNZOYF3JRlqt0IZgBZWfC3v8Hxx8Mll8CCBd7uiRbQjDGmjyUtmCU4NMou4KtAr0zj3OUL013JyYEXXoCZM+GCC+DvfwcsoBljTF9LZsmsy6FRVHWnqi4BgknMR7P9Dmbg3rb+xz/gyCPhvPPgpZcAC2jGGNOXkhnMEhkaJSEicn10CJZQqKthwDoWDWZdvmfWlYIC+Oc/4dBDYc4cK6EZY0wfS2YwS2RolISo6n3RIVj8fn+3M9QjJbOowkJYuBAOO8wFtIcfBiygGWNMX0hmMEtkaJReFQ1mRUU9dMMhQ+Df/4ZPf9q9fzZ3LqhaQDPGmF6WzGCWyNAovaqiwvXhyMrqwZvm5blqxosugm99C77+dYhELKAZY0wvSlowS2RoFBEZLiJlwNeB74hIWcwIzD2uvLyHqhjbSk93L1N/9atukOLLLoOmpnYBrbp6SRJ+uDHGmJQazurMM2HHDli6tOtzu0UVfvxjV0I79VQ3ckheHvX16ykp+RSNjVsYOfJ6xo//Hunp+9sLxRhjEjfQh7MaEMEsGAxSVlZGQ0NDp9decMF48vPD3Hdfaafn7beaGqisdCW2oUPB50M1Qii0h3B4LyD4/QX4fHlIp3PR9J7MzExGjx5NIBDo66wYY5JgoAez7ncNPICUlZWRl5fH+PHjOw0OtbXu9bBDDz00+ZnaswfWr3eltYMOgowMAMLhehobSwmHqxGJkJk5Br8/P/n56YSqUllZSVlZGRMmTOjTvBhjTHcMiLEZGxoaKCoq6rKUs8+DDO+PggKYMgVCIVi1CurqAPD5ssjKmkxW1iQA6uvXUle3hnC4vpcy1p6IUFRU1GXJ1hhjDlQDIpgBXQaypiaoru6BF6b3RW6um3MGYPVqN8pxJIKIq2bMyZlKRsYYwuFa6uo+oKFhM5FI918K3x8HSnWnMcZ0x4CoZkxEZaVb9lrJLCorywW0detgwwY3i3VhIRQVITk5pKcPw+8vpKlpK8HgToLBXWRkjCQQKMINb2mMMaYrA6Zk1pUeHf2jjT179vDb3/624xMyMmDqVJg8GfLzXWRdtQpWrOCMk0+munwXmZnjyM6eis+XRWPjZmpq3qOhYTPhcF2rW915553cfXevjMtsjDH9RsqUzHojmH3pS19qdywcDuPz+UDEBbL8fDex2u7dUFnJCz/+MZSWwu7d+IqKyBo8kTD1BIPlXtpJWloOgUAxgUBh4plShYYGqKpyqaHBVXsOHgyDBsF+DAtmjDEHmgH3jbZ27S3U1JS0279kyUnAXZSXX8m7727cp3vm5k5n8uRfdHj89ttvZ/369UyfPp1TTjmFM888k7vuuosRI0ZQUlLCypUrmTNnDqWlpTQ0NHDzzTdz/fXXQ3Ex48eNY+nzz1OzaROzZ8/mE9On8/qKFYwaOZK/PPF7AoOEJt1DY+MmGhtLCYX2EImkA1BSUsKNN95IXV0dEydO5KH772ew38+vfvEL5j/+OP60NKZOmMCTP/85r65cyc133gmqiAiL//hH8kaPdh1V0tP36ffRrzQ0uJcLx43r65wYc8ATkdOBXwI+4AFVndvmeD7we2AsLn7craoPi8gY4DFgOBAB7lPVX3rX3AlcB5R7t7lDVV/o6bwPuGDWkRkzSpg37xuMHLmtx+89d+5cVqxYQUlJCQCLFi3i7bffZsWKFc1d3R966CEKCwupr69n5syZnHfeeRQVFbkS2/DhkJPD2tJS/vjb33L/qFF84Rvf4NkHH+bSM87ALwIZ6UTSgb11hGUH9ZUruOyyi/n13T9j1rTp/N/3v89dX/0qv/j615l77718tGgRGUOHskcVhg7l7ttv594HH+SE6dOp2bKFzPp62LzZpexsF9SamlyJrr93BlGFt9+GRx6BJ590r0nccAP87GfuWY0x7cTMQXkKbmzdJSLynKqujDnty8BKVT1LRIYAq0XkD0AI+IaqLhORPOAdEVkYc+3PVTWp7SMDLph1VoL69Kd7Lx/HHntsq3e2fvWrX7HAm526tLSUtWvXumAWJcKECROYPns2AMd8+tNsrK+H8eORhgZoaMBXX4+vHgJA0/sVVJXvYlZRMZSVcdlnZ3PB/9wOBx/MkUcfzSXf/S5z5sxhzpw5AJxwwgl8/Rvf4JJLLuHcc89l9JQpUF/vvuj37IGtW11d7DnnwKWXwhe/CCNG9M4vq6ds2QKPP+6C2OrVrvPNeee5QP2b38Crr8ITT8BRR/V1To05EDXPQQkgItE5KGODmQJ54ro/5+ImWA6p6jZgG4Cq7hWRD3FTfsVem1Qp0wGkt+XktLxov2jRIl5++WXeeOMNli9fzlFHHRX3na4M78VqAF8gQCgtzTXyjR4NkybBEUcgI0bA8OHo+PHg8xEcOYi6ienUjQwR8YWo9W3h2QUPcuON1/HOO+9wzDHHEAqFuP3223nggQeor6/nuOOOY9WqVe7LfsQINy/btGmul+WUKfCDH7hquSuugHff7Y1fV/fV18Mf/winnQZjx7qhxIYOhQcegO3bXXD79a/h5Zfduxkf+xjccw9EIn2dc2MONInMQfkb4FDcDCjvAzeraqv/TCIyHjgKeCtm900i8p6IPCQig3s642DBrEfk5eWxd+/eDo9XVVUxePBgsrOzWbVqFW+++Wb3f5gIBAIUjB/P4OJi3ly/g6yCI3jmmSWceOKJRCJhNmxYxsc+VsR3v3s5e/bsoqpqO+vWreOII47gtttuY8aMGS6YxQoEWmbRXrMGbrwRnnkGjj4aZs2Cv/7VdVzpK6GQKzmuWQNvvgnPPQfXX++qaC++2JXEvvMd9wrE4sVwzTWuo0vUySfDe++5ATq/+U04/XTY1vNVzsYcwPzRSY69dH2b44nMQXkaUAKMBKYDv4kdHF5EcoFngFtUtdrbPQ+Y6J2/DbhnP58jrgFXzdgXioqKOOGEEzj88MOZPXs2Z555Zqvjp59+OvPnz+fII49kypQpHHfccT3ycx999NHmDiAHHXQQDz/8MJmZudx44w3s2bOLSCTMl750Aenp27n77nv4z3/ewecLMHXqVE4//fSObzxpEvzqV/C978GDD7r1OXNg4kQ3M8BVV7nAtz9CIdi501VvbtvWkrZvdy+Xx6bdu12PzLays+Hzn3clyJNOgrQu/jYrKoJnn4X774dbboEjjoCHHoLPfW7/nsWYZNu9Gx59FI45Bj75ye7eJaSqMzo5nsgclFcBc9UN6rtORD4CDgHeFpEALpD9QVWfjV6gqjui6yJyP/D37j5AZwbEQMMffvhh74y32A9FIiHC4SpCoSrC4b2oBr0jPny+XHy+PPz+XNLSslm1anX832MoBAsWwM9/Dm+84Uo8117rSj+BgBv0Ml6qq2tZLy93wSoavHbudB012ioqclWrhYXuNYLCwvhp8GA4/HD3ukF3rFrlSnTvvuvaB+++2zqHmAOLKixZAvPmuY5MDQ1w663wk59063ZdDTQsIn5gDXAysAU3J+XFqvpBzDnzgB2qeqeIDAOWAdOASuBRYJeq3tLmviO8NjVE5GvAx1T1wm49RCcsmKUQVUW1iXB4L6FQjRfcGr2jaWzYUE1+/qvk5X2MvLyjSE8f1v4mb73l5mz7858Tr3b0+12AGjHCpZEj468PG9a7rwk0Nrqqybvvdu2Gf/yjazs8kIXDrpSam9uzv6uB0It1oKitdf8W582DZcvcjMKXXOKq/vej81Iio+aLyBnAL3Bd8x9S1R9G559U1fkiMhJ4BBiBq5acq6q/F5FPAP/BtaNF29DuUNUXRORxXBWjAhuBG6LBrSdZMEtxkUiQcHgv4XANq1atY8+ezxCtJk9PH0Fu7nRyc49qXmZlHYRImnvR+5//dF+oOTnxU3a2Wx7o77EtXOiqKisr4TOfcYEiNv/RZdv1/HzXUzK6zM5OPCBEg1JVVUuPUu9Feioq3DLe+u7dLvCkpcGECXDwwW5kmYMPbkljxsSvcg0G3asY69a1pPXrW4Zay8iAUaNch6O2y+h6cXHHzxgOu9c7gsGWpdcTl4YG11kndhm7Hom4gVOHDnV/1Awb5krpXVUdR6m632V5uUsVFS4PhxziOjV1d2qjSMSV4t9+26XS0va/97Fj3TB1++vDD10Ae+wx9yyHHeZqDS691P0b208DfQoYC2am2YcffsjkySOpqSlh7953qal5l5qaEurqVuImDgefL4/c3GlecDuaQYM+Tnb2lP4/UHFFhesY8v77ratI6+rcl20i/P6WUV6iQS4/31XTRgNWNHh10mEIcD1No1WuRUWt1wsLXXBbu9Z1iFmzxuU1KiPDtXsefLALEJs2uYC1cWPr0nRWljtv0iQ3TVEwCGVl7hWHsjJXHdy212dGhstDONw6aEXfUexJPp/L/7BhLUFu6FD3s6JBKxq4KipcPuIJBFxQO/JI104aTaNHtw/MW7a4oPXWW265dGnLZ5WX53r5btzo5iyMSk93v7/YADd5cusOSND6Z8Wur14N8+e7V0fS0+H8810p7BOf6NHSsgWzA4wFs+Tp6PcYDjdQV7eyObjt3fsutbXLCYfdf2i/v5D8/OMZNOgE8vNPIC9vBj5fVm9nP3nCYVeCiG0LrKlpCUwdLaPrgYALbrGluNgUuy8auPal/U7VBZ7Y4LZmjdveuRPGj28JWhMntqwPH975l2Uo5EZPiQ1wW7a4wBEItKT09PjLQAAyM13Kymq9bLsPXGDasaMl7dzZeju6LyPDBbkhQ1yAj6633efzwcqV7g+UaCqN6XleUODaXQ87zN377bddmy64vE+bBsce25KmTHElRVV3fvR3HLtct85VX++rCRPci/1XXeUCdhJYMDvAWDBLnn35PapGqKtbTXX161RVvU5V1WvU168GQCRAbu7R5Oef0BzkMjKGJzPrxiRmzx5YsaJ1gPvgAxf8YgPXtGku0O6rSMQF/bVrm+cwBFqXWtt+5w4e7EphiVapdpMFswOMBbPk2d/fY1NTBdXVb1BV9RrV1a9RXb2kuYOJ3z+Y9PQRZGSMJD19BOnpI8nIcEu3fwTp6SPw+axHoTHJMNCDmb1n1kdyc3Opia1372J/f5CeXkxx8VkUF58FQCTSyN6971Jd/Tr19etpatpGU9M29ux5laambTGvCbTw+weTlTWZ7OwpZGUdTHb2FG99kgU6Y0yHLJiZpElLyyA//zjy89u/JK6qBIOVzQGusXGrtyylrm4Ne/b8mx07Hm91TUbGWLKzDyYrawrZ2QeTmXkQmZljyMgYg98/uP93QjHGdNvAC2a33ALe6PU9Zvp0925VB2677TbGjRvXPJ/ZnXfeSV5eHjfccANnn302u3fvJhgM8oMf/ICzzz47oR+pqvzP//wP//jHPxARvvOd73DBBRewbds2LrjgAqqrqwmFQsybN4/jjz+ea665hqVLlyIiXH311Xzta1/rgQdPHhEhPb2Y9PRi4Ii454TDtdTVraW+fg11daupq1tNff0adux4nHC4utW5aWnZZGaOJSNjTHNq2R5NevoI/P58C3jGDFADL5j1gQsvvJBbbrmlOZg99dRTvPjii2RmZrJgwQIGDRpERUUFxx13HJ/73OcS+kJ99tlnKSkpYfny5VRUVDBz5kxOPPFEnnjiCU477TS+/e1vEw6Hqauro6SkhC1btrBixQrATRY6EPh8OeTlTScvb3qr/apKU9MOGhs30dBQSmPjZhobS731UmprV9DUtJ22w8qJZJCePrzTlJExivT0EaSl2X8NY/qTgfc/tpMSVLIcddRR7Ny5k61bt1JeXs7gwYMZO3YswWCQO+64g8WLF5OWlsaWLVvYsWMHw4d33bPvv//9LxdddBE+n49hw4Zx0kknsWTJEmbOnMnVV19NMBhkzpw5TJ8+nYMOOogNGzbwla98hTPPPJNTTz21F56674gIGRnDycgYzqBBH4t7TiTSRGPjFhobS2lsLKOpabuXdtDUtJ2Gho+orn6DYLCc9mOp+sjIGOWV7MY2L1tKe2OtlGfMAWbgBbM+cv755/P000+zfft2LrzQDTv2hz/8gfLyct555x0CgQDjx4+PO/VLPB31Mj3xxBNZvHgxzz//PJdddhm33norl19+OcuXL+ell17i3nvv5amnnuKhhx7qsWfrj9LS0snKmkBW1oROz4tEQgSD5V6g2+YFwM00NGymsXEz1dVvUl7+53adVdLSsvH7B+P35+P3F7Rb+nz5zevp6cPIyBhNRsZo68RiTJJYMOshF154Iddddx0VFRW8+uqrgJv6ZejQoQQCAf7973+zadOmhO934okn8rvf/Y4rrriCXbt2sXjxYn7605+yadMmRo0axXXXXUdtbS3Lli3jjDPOID09nfPOO4+JEydy5ZVXJukpB560ND8ZGe7VADcFU3uqEa9asyXINTZuIRTaQyjkBnFuatpJff3a5n3xemqCe8E82o6XkTHa68AymoyMMaSnjyQQGIzPl4/P1413nIxJYRbMeshhhx3G3r17GTVqFCO8GZovueQSzjrrLGbMmMH06dM55JBDEr7fOeecwxtvvMG0adMQEX7yk58wfPhwHn30UX76058SCATIzc3lscceY8uWLVx11VVEvKGHfvSjHyXlGVOVSFpzwOuoWjOWqhKJ1HuBbg9NTdtpbCxrrvKMLqur3yQUquzgZ2Z4Jb3YUl/seiGBQDGBQFHMsgi/v5C0tG6OQ2hMP2YvTZtm9nvsfeFwfXPbXlPTNq9kt6c5ELZdD4erCAZ3x8x20J7Pl98c4Pz+waSlpSPiRySASIC0tEDzukt+0tIC+P0FBALDSE93KRAYSnr6UNLSDvCBok1C7KVpY0zS+HxZZGdPIjt70j5dFw7XEQxWEgxWNC9DodbbwWAlodAuVINEIkFUg6iGvGXsvpZj8bjRW6LBbRiBwBB8vjxvPrzOk2s/zLHOMibpLJgZ0w/5fNn4fNlkZo7p+uQEhUI1BIM7aGraSVPTjjjrO6ipeY9gsIJwuKbT0mEskYBXLVrYXBUaCBS2Wvr9g4A0QLzA15JE0mLWBZ9vkFfydMmqVQ0MoGCmqvbX337ob9XNpuf5/bn4/blkZU1M6Hw3F14t4XBNB2mvVz26i2BwV/OyoWETNTXvEgzuIhKp7foHdaGlWrWY9PQhzetuVJgAIj6vmjWaWrbB51WzZuLzZZOWlu0ts1pti6Tb98sBbkAEs8zMTCorKykqKrJ/cN2gqlRWVpLZnVHCTcpKSwuQllZAIFDQ7XtEIo0Eg7sJh6u8P6haUst2pNV6KLTXq0Yt95YtqbFxCzU1ywkGy4lEEpyHLiFpXoDLQiTDa4dM73TpXt/Iw+cbhM+X56277ZZ1V12blpZBWlomaWkZ3v0HxFdzrxoQHUCCwSBlZWUJv8Nl2svMzGT06NEEujsjrzEHmEikyWsjbJvCbbaDRCKNRCL1hMN1RCJ13rK+1Xr0mDu/CdWmTpaNXql1L+Hw3m4E1jQvwEWDmwt2I0fewJgxX+/W78M6gPQDgUCACRM6fznWGJNaXC/MA6MnpquS3etVve711qu9ddf+6AKqS623G5r3pafbvIAdSWowE5HTgV8CPuABVZ3b5rh4x88A6oArVXVZMvNkjDG9zVXJug4vB7IEvrPzgd8DY3Hx425Vfbiza0WkEPgTMB7YCHxBVXf3dN6TNrWpiPiAe4HZwFTgIhGZ2ua02cBkL10PzEtWfowxxnQswe/sLwMrVXUaMAu4R0TSu7j2duAVVZ0MvOJt97hkztN9LLBOVTeoahPwJNB2/pOzgcfUeRMoEJERScyTMcaY+BL5zlYgz6tVywV2AaEurj0beNRbfxSYk4zMJ7OacRRQGrNdBrQdCyjeOaOAbbEnicj1uJIbgIpIfTfz5Mf94geSgfZMA+15YOA900B7Hhh4zxTvebJEZGnM9n2qel/MdiLf2b8BngO2AnnABaoaEZHOrh2mqtsAVHWbiAztzgN1JZnBLF4f+bZdJxM5B+8Xfl+cc/ctQyJLVXXG/t7nQDLQnmmgPQ8MvGcaaM8DA++Zuvk8iXwfnwaUAJ8GJgILReQ/CV6bVMmsZiwDYocnGI2L5vt6jjHGmORL5Pv4KuBZr2loHfARcEgX1+6INh95y51JyHtSg9kSYLKITBCRdOBCXPE01nPA5eIcB1RFi6PGGGN6VSLf2ZuBkwFEZBgwBdjQxbXPAVd461cAf01G5pNWzaiqIRG5CXgJ11XzIVX9QERu9I7PB17Adctfh+uaf1Wy8uPZ76rKA9BAe6aB9jww8J5poD0PDLxn2ufnSfA7+/vAIyLyPq5q8TZVrQCId61367nAUyJyDS4Yfn7/Hi2+fjcCiDHGGNNWMqsZjTHGmF5hwcwYY0y/lzLBTEROF5HVIrJORJLyBnpvE5GNIvK+iJS0eX+kXxCRh0Rkp4isiNlXKCILRWSttxzcl3ncVx08050issX7nEpE5Iy+zOO+EJExIvJvEflQRD4QkZu9/f3yc+rkefrzZ5QpIm+LyHLvme7y9vfLz6i7UqLNzBtqZQ1wCq4L6RLgIlVd2acZ208ishGYEW2A7W9E5ESgBjcKzOHevp8Au1R1rvdHx2BVva0v87kvOnimO4EaVb27L/PWHV5X6hGqukxE8oB3cCM4XEk//Jw6eZ4v0H8/IwFyVLVGRALAf4GbgXPph59Rd6VKySyRYVpML1PVxbjhcGL1ytA3ydLBM/VbqrotOvi3qu4FPsSNFNEvP6dOnqff8t75qvE2A15S+uln1F2pEsw6Gjarv1PgnyLyjjfk10DQaugbIClD3/SBm0TkPa8asl9W94jIeOAo4C0GwOfU5nmgH39GIuITkRLcC8kLVXVAfEb7IlWCWZ8PtZIkJ6jq0biRqr/sVXGZA8883NA/03Hjjt7Tp7npBhHJBZ4BblHV6r7Oz/6K8zz9+jNS1bCqTseNvHGsiBzex1nqdakSzAbksFmqutVb7gQW4KpT+7teGfqmN6nqDu/LJgLcTz/7nLx2mGeAP6jqs97ufvs5xXue/v4ZRanqHmARcDr9+DPqjlQJZokM09KviEiO14CNiOQApwIrOr+qX+iVoW96k7Se1ugc+tHn5HUueBD4UFV/FnOoX35OHT1PP/+MhohIgbeeBXwGWEU//Yy6KyV6MwJ4XW1/QctQKz/s2xztHxE5CFcaAzcs2RP97ZlE5I+4Cf6KgR3Ad4G/AE/hZrLdDHxeVftNh4oOnmkWrvpKcTPt3tBfxiAVkU8A/wHeByLe7jtw7Uz97nPq5Hkuov9+RkfiOnj4cAWUp1T1eyJSRD/8jLorZYKZMcaYgStVqhmNMcYMYBbMjDHG9HsWzIwxxvR7FsyMMcb0exbMjDHG9HsWzIzpRSIyS0T+3tf5MGagsWBmjDGm37NgZkwcInKpN0dUiYj8zhvItUZE7hGRZSLyiogM8c6dLiJveoPULogOUisik0TkZW+eqWUiMtG7fa6IPC0iq0TkD96oFMaY/WDBzJg2RORQ4ALcQM7TgTBwCZADLPMGd34VN7oHwGPAbap6JG5kiej+PwD3quo04HjcALbgRmq/BZgKHASckORHMmbA8/d1Bow5AJ0MHAMs8QpNWbhBWiPAn7xzfg88KyL5QIGqvurtfxT4szdu5ihVXQCgqg0A3v3eVtUyb7sEGI+bUNEY000WzIxpT4BHVfVbrXaK/G+b8zobC66zqsPGmPUw9v/QmP1m1YzGtPcKcL6IDAUQkUIRGYf7/3K+d87FwH9VtQrYLSKf9PZfBrzqzZFVJiJzvHtkiEh2bz6EManE/iI0pg1VXSki38HN4p0GBIEvA7XAYSLyDlCFa1cDN73GfC9YbQCu8vZfBvxORL7n3ePzvfgYxqQUGzXfmASJSI2q5vZ1Powx7Vk1ozHGmH7PSmbGGGP6PSuZGWOM6fcsmBljjOn3LJgZY4zp9yyYGWOM6fcsmBljjOn3/j+msC3AzSDjUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,loss_ax=plt.subplots()\n",
    "acc_ax=loss_ax.twinx()\n",
    "\n",
    "loss_ax.plot(hist.history['loss'],'y',label='train loss')\n",
    "loss_ax.plot(hist.history['val_loss'],'r',label='val loss')\n",
    "loss_ax.set_ylim([0.0,0.5])\n",
    "\n",
    "acc_ax.plot(hist.history['accuracy'],'b',label='train acc')\n",
    "acc_ax.plot(hist.history['val_accuracy'],'g',label='val acc')\n",
    "acc_ax.set_ylim([0.8,1.0])\n",
    "\n",
    "loss_ax.set_xlabel('epoch')\n",
    "loss_ax.set_ylabel('loss')\n",
    "acc_ax.set_ylabel('accuracy')\n",
    "\n",
    "loss_ax.legend(loc='upper left')\n",
    "loss_ax.legend(loc='lower left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 97us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04014839279784501, 0.9882000088691711]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_and_metrics=model.evaluate(x_test,y_test,batch_size=32)\n",
    "loss_and_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat_test=model.predict(x_test,batch_size=32)\n",
    "\n",
    "plt_row=5\n",
    "plt_col=5\n",
    "plt.rcParams['figure.figsize']=(10,10)\n",
    "f,axarr=plt.subplots(plt_row,plt_col)\n",
    "\n",
    "cnt=0\n",
    "i=0\n",
    "while cnt < (plt_row*plt_col):\n",
    "    if np.argmax(y_test[i])==np.argmax(yhat_test[i]):\n",
    "        i+=1\n",
    "        continue\n",
    "    \n",
    "    sub_plt=axarr[cnt//plt_row,cnt%plt_col]\n",
    "    sub_plt.axis('off')\n",
    "    sub_plt.imshow(x_test[i].reshape(width,height))\n",
    "    sub_plt_title='R: '+str(np.argmax(y_test[i]))+' P: '+str(np.argmax(yhat_test[i]))\n",
    "    sub_plt.set_title(sub_plt_title)\n",
    "    \n",
    "    i+=1\n",
    "    cnt+=1\n",
    "    \n",
    "plt.show()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
